Journal of Plant Ecology ›› 2025, Vol. 18 ›› Issue (5): 1-11.DOI: 10.1093/jpe/rtaf106

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植被研究中量化和比较观察者误差的指标体系

  

  • 收稿日期:2025-02-09 接受日期:2025-06-09 出版日期:2025-10-01 发布日期:2025-10-06

Metrics for quantifying and comparing observer error across vegetation studies

Lloyd W. Morrison1,2,*, Sherry A. Leis2 and Michael D. DeBacker2   

  1. 1Department of Biology, Missouri State University, Springfield, MO 65897, USA
    2Heartland Inventory and Monitoring Program, National Park Service, Republic, MO 65738, USA

    *Corresponding author. E-mail: lloydmorrison@missouristate.edu
  • Received:2025-02-09 Accepted:2025-06-09 Online:2025-10-01 Published:2025-10-06
  • Supported by:
    This work was supported by the Inventory and Monitoring Program of the National Park Service.

摘要: 观察者误差作为一类非抽样误差,在植被调查中普遍存在,且往往对结果产生显著影响。研究者应在发表成果时同步报告观察者误差率,然而目前缺乏标准化、便于比较的标准化报告格式。本文系统阐述了观察者误差(即观察者间的不精确度)相关的5项关键指标及其计算方法、报告规范和解读方式。其中3项指标适用于物种组成分析,包括伪周转率、物种丰富度观察者偏差和真实物种丰富度低估率;另两项指标(盖度一致性率和盖度估计观察者偏差)则适用于植被盖度分类估算。这些指标均具有以下特征:计算简便、适用于需两名及以上观察者参与的任意多物种调查数据,且便于跨研究比较。所有指标均以百分比形式呈现,使得物种多样性差异巨大的研究之间也能进行相对比较。本文还介绍了如何将物种组成和盖度估计中的误差分解为随机误差与系统性偏差,这有助于判断特定观察者是否需要补充培训。5项指标中的伪周转率和盖度一致率在以往研究中已有应用,本文汇总了不同生境类型的伪周转率文献数据以及盖度一致性分级标准,便于与未来研究进行比较。最后,本文通过3位观察者采集的真实数据集演示了这些误差指标的计算流程。

关键词: 盖度一致性, 盖度估计, 双重抽样, 非抽样误差, 观察者偏差, 观察者误差, 伪周转率

Abstract: Observer error, a type of nonsampling error, is pervasive in vegetation sampling and often of a consequential magnitude. Observer error rates should be reported along with published studies, although there currently exists no standardized, easily comparable format. Here we describe five key metrics of observer error (i.e. imprecision between observers), how they are calculated, and how they can be reported and interpreted. Three metrics apply to species composition: pseudo-turnover, observer bias in species richness and underestimation of true species richness. Two metrics—cover agreement and observer bias in cover estimation—apply to categorical cover estimation. All metrics are simple to determine, could be calculated from virtually any multispecies sampling effort using two or more observers, and are easily compared with other studies. The metrics are all reported as percentages, allowing for relative comparisons among studies with greatly differing species diversities. We also describe how to decompose the amount of error in species composition and cover estimation into random and biased components. Such decomposition is useful in determining whether additional training may be necessary for some observers. Two of the five metrics—pseudo-turnover and cover agreement—have been quantified in previous studies, and we compile a list of published rates of pseudo-turnover within general habitat types, and published cover agreement categories, for comparison with future studies. Finally, we provide an example by calculating the observer error metrics for a real data set collected by three different observers.

Key words: cover agreement, cover estimation, double sampling, nonsampling error, observer bias, observer error, pseudo-turnover